def task_predict(input_files, input_model):
# 把输入的多个模型目录字符串分离为目录列表
input_models = [os.path.expanduser(k) for k in input_model.strip().split()]
# 把各个目录下的模型列表解压出来组合成一个迭代器
models = itertools.chain(*(glob.glob(m) for m in input_models))
# 生成并加载包括所有模型文件（skgmm.GMMSet object）的列表
models = [ModelInterface.load(m) for m in models]
if len(models) == 0:
print("No model file found in %s" % input_model)
sys.exit(1)
# 定义统计准确率的变量
right = 0
right1 = 0
wrong = 0
wrong1 = 0
num = 0
# 对每个预测音频文件提取特征并与每个模型匹配得到TOP结果
for f in glob.glob(os.path.expanduser(input_files)):
start_time = time.time()
fs, signal = read_wav(f)
print(f)
feat = get_feature(fs, signal)
#print("Get feature ", time.time() - start_time, " seconds")
predict_result = []
f_models = [(feat, m) for m in models]
#print(models)
# 每个音频文件分别匹配每个模型组并得出分数放到总列表
# for model in models:
# #start_time1 = time.time()
# #print(model)
# # 模型文件是一个元组：(label,gmm)
# score = model[1].score(feat)
# label=model[0]
# result=(label,score)
# #print(results)
# predict_result.append(result)
#print("Get one score ", time.time() - start_time1, " seconds")
pool = ThreadPool(2)
predict_result = pool.map(get_score, f_models)
pool.close()
pool.join()
#print(results)
#print("Get score ", time.time() - start_time, " seconds")
proba = GMMSet.softmax([i[1] for i in predict_result])
predict_result = [(predict_result[i][0], proba[i])
for i in range(len(proba))]
#print("predict_result:",predict_result)
# 对预测结果按分数作高到底排序
predict_result = sorted(predict_result,
key=operator.itemgetter(1),
reverse=True)
#print("sort_predict_result:", predict_result)
# 微信语音数据集的label格式
label = os.path.basename(f).split('_')[0] #[6:11]
#label=os.path.basename(f).split('(')[0]#[6:11]
# AISHELL数据集的label格式
# label=os.path.basename(f)[6:11]
predict = predict_result[0][0]
predict_score = predict_result[0][1]
print("Predict ", time.time() - start_time, " seconds")
# #print('Top:',predict_result[:10])
# 统计top1准确率
if label in predict:
right1 += 1
print('label:', label, ' predict:', predict, ' score:',
predict_score, ' top1 right')
else:
wrong1 += 1
print('label:', label, ' predict:', predict, ' score:',
predict_score, ' top1 wrong')
# 统计Top10准确率
predicts = []
predict_scores = []
for pre in predict_result[:10]:
predicts.append(pre[0])
predict_scores.append(pre[1])
if label in predicts:
right += 1
print('label:', label, ' predicts:', predicts, ' scores:',
predict_scores, ' top10 Right')
else:
wrong += 1
print('label:', label, ' predicts:', predicts, ' scores:',
predict_scores, ' top10 Wrong')
num += 1
print('top1:', num, ' right:', right1, ' wrong:', wrong1, ' top1 acc:',
right1 / num)
print('top10:', num, ' right:', right, ' wrong:', wrong, ' top10 acc:',
right / num)
